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arxiv: 2512.07993 · v2 · submitted 2025-12-08 · 💻 cs.AI

SkipKV: Selective Skipping of KV Generation and Storage for Efficient Inference with Large Reasoning Models

Jiayi Tian , Seyedarmin Azizi , Yequan Zhao , Erfan Baghaei Potraghloo , Sean McPherson , Sharath Nittur Sridhar , Zhengyang Wang , Zheng Zhang
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Massoud Pedram Souvik Kundu
This is my paper

Pith reviewed 2026-05-16 23:49 UTC · model grok-4.3

classification 💻 cs.AI
keywords KV cache compressionlarge reasoning modelschain-of-thoughtinference efficiencysentence-level evictiontraining-free methodselective skipping
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The pith

SkipKV reduces KV cache overhead in large reasoning models by evicting similar sentences at inference time while steering toward shorter outputs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large reasoning models produce long chain-of-thought sequences whose key-value caches grow linearly and create memory and speed bottlenecks. SkipKV replaces token-level eviction with sentence-level removal of highly similar content, paired with a dynamic steering adjustment to hidden states that discourages repeated generation. The method requires no retraining and operates during inference to keep semantic coherence. A reader would care because the approach yields shorter sequences and higher final-answer accuracy than prior compression techniques at the same memory budget. It also sustains performance in the multi-batch settings where token-wise methods degrade.

Core claim

SkipKV is a training-free KV compression technique that scores sentences for similarity, removes redundant ones to shrink the cache, and applies a steering vector to hidden activations so the model produces concise chain-of-thought reasoning without losing correctness on downstream tasks.

What carries the argument

Sentence-scoring metric that flags highly similar sentences for eviction, together with a dynamic steering vector that updates hidden states to suppress redundant generation.

If this is right

  • Accuracy stays higher than token-level eviction baselines at identical compression ratios.
  • Output sequences become up to 1.6 times shorter than those from state-of-the-art methods.
  • Inference throughput rises by up to 1.7 times.
  • Multi-batch inference remains stable, unlike padding-sensitive token-wise approaches.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same sentence-level logic could be tested on long-context summarization or code-generation tasks.
  • Shorter generations would directly cut energy use in large-scale deployment.
  • Combining the eviction rule with quantization could produce additive savings without new training.

Load-bearing premise

Sentence-level similarity can be trusted to mark content whose removal leaves the reasoning chain and final answer intact.

What would settle it

A side-by-side run on any reasoning benchmark in which SkipKV removes similar sentences yet produces a different and incorrect final answer compared with the unpruned model.

Figures

Figures reproduced from arXiv: 2512.07993 by Erfan Baghaei Potraghloo, Jiayi Tian, Massoud Pedram, Sean McPherson, Seyedarmin Azizi, Sharath Nittur Sridhar, Souvik Kundu, Yequan Zhao, Zhengyang Wang, Zheng Zhang.

Figure 1
Figure 1. Figure 1: Comparison of KV cache eviction methods for a reason￾ing model. Marker size denotes KV memory usage. SkipKV yields shorter generation length while maintaining high accuracy under a smaller KV budget. it needs ∼2.5× larger KV cache memory compared to the model weights. In addition to the significant memory over￾head, the growing KV cache impacts the throughput of the memory bound decoding stage of LRMs. Thi… view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of KV cache eviction methods during token generation. Cached tokens marked with × indicate evicted positions. (a) SnapKV performs one-time eviction after prefill; (b) H2O evicts tokens with low cumulative attention scores; (c) R-KV prunes redundant tokens based on token-level similarity (purple); (d) SkipKV (ours) groups tokens within sentences (green) to evict high sentence-redundancy regions, … view at source ↗
Figure 3
Figure 3. Figure 3: Left: Accuracy comparison for single- and multi-batch decoding of H2O (Zhang et al., 2023) and R-KV (Cai et al., 2025). Center: Visualization of the prefill token length distribution of MATH-500, and the min-max range of each batch (batch-size, bs = 10). Right: Accuracy and generated token length versus KV budget with R-KV eviction on MATH-500 (bs = 10). R-KV (Cai et al., 2025) identifies a key limitation … view at source ↗
Figure 5
Figure 5. Figure 5: Statistics on the ratio of high-similarity sentences (top) and non-execution thoughts (bottom) generated for samples that the models answered correctly and incorrectly. sentences or reasoning segments) to achieve a better trade￾off between accuracy and generation length. 4 SKIPKV: METHODOLOGY In this sections, we first analyze the sentence-level proper￾ties of reasoning traces motivating the design of Skip… view at source ↗
Figure 6
Figure 6. Figure 6: Overview of SkipKV framework. It selectively skips KV-cache storage and generation by leveraging sentence-level redundancy detection. The central reasoning pipeline illustrates the end-to-end process from prefill to decoding, where input sentence ranges and types are recorded, sentences are scored, and the KV cache is compressed. The left panel depicts the adaptive steering mechanism used to skip KV genera… view at source ↗
Figure 7
Figure 7. Figure 7: Illustration of the cache range monitoring process over two consecutive compression steps. Colored blocks represent distinct sentence spans in the generation space and their corre￾sponding regions in the KV cache. Gray dashed lines indicate the mapping of sentence range. KV Cache Sentence Range Monitoring Logic. To en￾sure that sentences in the redundancy set P are evicted consistently from the cache space… view at source ↗
Figure 8
Figure 8. Figure 8: Batch grouping increases valid KV budget for high￾performance multi-batch decoding. Yellow blocks: valid tokens in the KV cache, Gray blocks: padding tokens. Inspired by SEAL (Chen et al., 2025) we first construct the steering vector using 500 samples from the MATH train￾ing set, aiming to shift the latent representations toward execution-style reasoning. The steering vector is computed as the mean latent … view at source ↗
Figure 9
Figure 9. Figure 9: Accuracy comparison under different KV-cache budgets for SkipKV, H2O, R-KV, and FullKV across three reasoning benchmarks and R1-Qwen-7B and 14B models. SkipKV consistently achieves higher accuracy under tighter KV budgets, maintaining full accuracy even at only 15 % KV budget on AIME-24. Results are reported as pass@1. ing. In experimental Section 5.3 we empirically validate the effectiveness of increased … view at source ↗
Figure 10
Figure 10. Figure 10: Total generation tokens on SkipKV under different KV budget with H2O, R-KV, and FullKV across three datasets and two models. SkipKV consistently generates fewer tokens and could achieve up to 30% fewer generation length compared with FullKV. 32%, 39%, and 48% fewer tokens on R1-Qwen-7B, R1- Qwen-14B, and R1-Llama-8B, respectively—translating to a 1.5 − 2× reduction in generation latency. Additionally, on … view at source ↗
Figure 11
Figure 11. Figure 11: KV-cache memory consumption and reasoning accuracy of SkipKV under different KV budgets, compared with SEAL and Full-KV baselines on AIME-24 across multiple reasoning models. Left: R1-Qwen-7B; Center: R1-Qwen-14B; Right: R1-Llama-8B. 0 200 400 Sample Index 0 200 400 600 800 Prefill Length Prefill Lengths Distribution 0 200 400 Sample Index 0 200 400 600 800 Prefill Length Prefill Lengths Distribution w/ G… view at source ↗
Figure 12
Figure 12. Figure 12: Visualization of the prefill token length distribution of MATH-500 and the min-max range of each 10 samples before (left) and after (right) using batch grouping. ate gains by removing redundant sentences, slightly im￾proving accuracy and shortening the generated sequence length. Including Adaptive Steering further enhances effi￾ciency by dynamically skipping non-execution thoughts, leading to a substantia… view at source ↗
Figure 13
Figure 13. Figure 13: Comparison of accuracy and total generation token length on SkipKV under different KV budget with H2O, R-KV, and FullKV across three datasets and R1-Llama-8B model. A.2 Experimental Setup Hyper-parameters. Following R-KV, we compress and update KV cache every 128 decoding steps and set the attention-redundancy score trade-off factor to σ = 0.1. The similarity threshold τ in the sentence-scoring metric is … view at source ↗
Figure 14
Figure 14. Figure 14: Comparison of the ratio of non-execution thoughts (top) and high-similarity sentences (bottom) generated by different methods on AIME-24, LiveCodeBench, and MATH-500 using R1-Qwen-7B. The boxplots show distributions for samples that were answered correctly (green) and incorrectly (red) of each method. A.5 Empirical Study of Generated Outputs In this section, we present qualitative examples from the MATH-5… view at source ↗
Figure 15
Figure 15. Figure 15: Qualitative example of R-KV responses on the MATH-500 dataset. The darkness of red denotes how many heads select the token. Non-execution sentences starting re-validation are highlighted in yellow , where each is followed by few execution thoughts, and the answers are highlighted in a blue box. R-KV frequently selects fragmented tokens within execution reasoning and always includes parts of the answer its… view at source ↗
Figure 16
Figure 16. Figure 16: Qualitative example of SkipKV responses on the MATH-500 dataset. SkipKV primarily evicts entire sentences instead of fragmented tokens, avoiding interrupting the reasoning path [PITH_FULL_IMAGE:figures/full_fig_p017_16.png] view at source ↗
read the original abstract

Large reasoning models (LRMs) often incur significant key-value (KV) cache overhead, due to their linear growth with the verbose chain-of-thought (CoT) reasoning. This incurs both memory overhead and throughput bottlenecks, limiting efficient deployment. To reduce KV cache size during inference, we first investigate the effectiveness of existing KV cache eviction methods for CoT reasoning. Interestingly, we find that due to unstable token-wise scoring and reduced effective KV budget caused by padding, state-of-the-art (SoTA) eviction methods fail to maintain accuracy in multi-batch settings. Additionally, these methods often generate longer sequences than the original model without eviction, as semantic-unaware token-wise eviction leads to repeated revalidation during reasoning. To address these issues, we present \textbf{SkipKV}, a \textbf{\textit{training-free}} KV compression method that performs selective \textit{eviction} and \textit{generation}, operating at a coarse-grained, sentence-level sequence removal for efficient CoT reasoning. In specific, it introduces a \textit{sentence-scoring metric} to identify and remove highly similar sentences while maintaining semantic coherence. To suppress redundant generation, SkipKV dynamically adjusts a steering vector to update the hidden activation states during inference, enforcing the LRM to generate concise responses. Extensive evaluations on multiple reasoning benchmarks demonstrate that SkipKV achieves up to $\mathbf{26.7}\%$ higher accuracy compared to baseline methods, at a similar compression budget. Additionally, compared to SoTA, SkipKV yields up to $\mathbf{1.6}\times$ shorter generation length while improving throughput by up to $\mathbf{1.7}\times$. Our code is released at: \href{https://github.com/TTTTTTris/SkipKV}{https://github.com/TTTTTTris/SkipKV}.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes SkipKV, a training-free KV cache compression method for large reasoning models that performs selective sentence-level eviction of highly similar sentences via a sentence-scoring metric and dynamically adjusts a steering vector during inference to suppress redundant generation. It reports up to 26.7% higher accuracy than baselines at comparable compression budgets, up to 1.6× shorter generation lengths, and up to 1.7× higher throughput across multiple reasoning benchmarks.

Significance. If the results hold under scrutiny, SkipKV would offer a practical advance for deploying verbose chain-of-thought models by addressing KV cache growth without retraining. The combination of maintained or improved accuracy with reduced length and higher throughput is notable, as most eviction techniques incur accuracy penalties; the open-sourced code further strengthens potential impact for efficient LRM inference.

major comments (3)
  1. [Abstract] Abstract and method description: the sentence-scoring metric used for eviction is introduced only at high level with no equation, pseudocode, or explicit similarity computation (e.g., embedding model or cosine threshold formula), which is load-bearing for verifying whether evicted sentences are truly redundant versus critical CoT steps.
  2. [§3] §3 (steering vector adjustment): the update rule and magnitude selection for the steering vector are described qualitatively without a concrete equation or sensitivity analysis, leaving open whether the reported gains depend on post-hoc tuning of the free parameters (sentence similarity threshold and steering vector magnitude) rather than being robustly training-free.
  3. [Evaluation] Evaluation section: no per-task accuracy breakdown, threshold sensitivity curves, or analysis of evicted sentences is provided to confirm that similarity-based removal preserves semantic coherence and final-answer correctness, undermining the central claim that the method avoids introducing new reasoning errors.
minor comments (2)
  1. [Abstract] The abstract lists concrete gains but omits the specific benchmarks and model sizes used; adding these would improve clarity.
  2. [Figures/Tables] Figure captions and tables could more explicitly state the compression budget and batch size settings to allow direct comparison with the SoTA baselines mentioned.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address each major comment below and have prepared revisions to improve the clarity and completeness of the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract and method description: the sentence-scoring metric used for eviction is introduced only at high level with no equation, pseudocode, or explicit similarity computation (e.g., embedding model or cosine threshold formula), which is load-bearing for verifying whether evicted sentences are truly redundant versus critical CoT steps.

    Authors: We agree the description was insufficiently precise. In the revised manuscript we will add the explicit sentence-scoring equation (average cosine similarity of Sentence-BERT embeddings), the fixed similarity threshold, and pseudocode for the eviction step. These additions will make it straightforward to verify that only redundant sentences are removed. revision: yes

  2. Referee: [§3] §3 (steering vector adjustment): the update rule and magnitude selection for the steering vector are described qualitatively without a concrete equation or sensitivity analysis, leaving open whether the reported gains depend on post-hoc tuning of the free parameters (sentence similarity threshold and steering vector magnitude) rather than being robustly training-free.

    Authors: We will insert the exact update rule equation for the steering vector (additive adjustment to hidden states). The magnitude is a single fixed hyper-parameter chosen once on a small held-out validation set and held constant across all experiments; no per-task or per-instance tuning occurs. We will also add a sensitivity plot over a range of magnitudes and thresholds to demonstrate robustness. revision: partial

  3. Referee: [Evaluation] Evaluation section: no per-task accuracy breakdown, threshold sensitivity curves, or analysis of evicted sentences is provided to confirm that similarity-based removal preserves semantic coherence and final-answer correctness, undermining the central claim that the method avoids introducing new reasoning errors.

    Authors: We will expand the evaluation section with per-task accuracy tables, threshold sensitivity curves, and qualitative examples of evicted sentences together with the corresponding final-answer correctness. These additions directly support the claim that semantic coherence and answer accuracy are preserved. revision: yes

Circularity Check

0 steps flagged

No circularity; training-free method with external benchmarks

full rationale

The paper presents SkipKV as a training-free approach using sentence-level similarity scoring for KV eviction and a steering vector for concise generation. No equations, parameters, or claims reduce the reported accuracy gains, length reductions, or throughput improvements to quantities fitted inside the same experiments or to self-referential definitions. Evaluations rely on external reasoning benchmarks, and the core operations do not invoke self-citations as load-bearing uniqueness theorems or smuggle ansatzes. The derivation chain is self-contained against independent data.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The approach rests on two main heuristics whose parameters are not derived from first principles: a sentence similarity threshold and the magnitude of the steering vector adjustment. These are treated as tunable knobs whose values are chosen to achieve the reported compression-accuracy trade-off.

free parameters (2)
  • sentence similarity threshold
    Determines which sentences are considered redundant enough to evict; value chosen to balance compression and accuracy.
  • steering vector magnitude
    Controls how strongly hidden states are adjusted to suppress redundant generation; tuned for conciseness.
axioms (1)
  • domain assumption Highly similar sentences in CoT reasoning can be removed while preserving overall semantic coherence and final answer correctness.
    Central premise of the sentence-scoring eviction strategy.

pith-pipeline@v0.9.0 · 5681 in / 1206 out tokens · 31984 ms · 2026-05-16T23:49:37.800693+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

7 extracted references · 7 canonical work pages · 1 internal anchor

  1. [1]

    ThinKV: Thought-Adaptive KV Cache Compression for Efficient Reasoning Models

    Accessed: 2025-08-30. Ramachandran, A., Neseem, M., Sakr, C., Venkatesan, R., Khailany, B., and Krishna, T. Thinkv: Thought-adaptive kv cache compression for efficient reasoning models. arXiv preprint arXiv:2510.01290, 2025. Reimers, N. and Gurevych, I. Sentence-bert: Sentence em- beddings using siamese bert-networks. InProceedings of the 2019 Conference ...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  2. [2]

    Expanded \( x(1+x) \) to \( x + x^2 \). Correct

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  3. [3]

    Added \( 2x \) to get \( x + x^2 + 2x = x^2 + 3x \). Correct

    work page

  4. [4]

    Distributed the outer x: \( x(x^2 + 3x) = x^3 + 3x^2 \). Correct

    work page

  5. [5]

    Distributed the -3: \( -3x^2 + 3x - 6 \). Correct

    work page

  6. [6]

    Combined all terms: \( x^3 + 3x^2 - 3x^2 + 3x - 6 \). Correct

    work page

  7. [7]

    Simplified: \( x^3 + 0x^2 + 3x - 6 \), which is \( x^3 + 3x - 6 \). Correct. I don't think I missed anything. So, the simplified expression is \( x^3 + 3x - 6 \). Just to be thorough, let me plug in a value for x and see if the original expression and the simplified one give the same result. Let's choose x = 1. Original expression: \( 1*(1*(1+1) + 2*1) - ...

    work page